Title :
An algorithm for a least-square approximation problem of unknown systems
Author :
Maeda, Yutaka ; Kanata, Yakchi
Author_Institution :
Dept. of Electr. Eng., Kansai Univ., Osaka, Japan
fDate :
28 Oct-1 Nov 1991
Abstract :
The authors consider a problem of finding a least-squares approximation parameter that minimizes the output error of unknown systems. When the dimension of the output is equal to the dimension of the input, one can apply the stochastic approximation algorithm. On the other hand, if the dimension of the output is greater than the dimension of the input, one cannot use stochastic approximation. The authors propose an algorithm that is applicable to this problem. This algorithm is an extension of the Robbins-Monro stochastic approximation procedure. A convergence theorem for this proposed procedure is demonstrated
Keywords :
convergence of numerical methods; least squares approximations; Robbins-Monro stochastic approximation; convergence theorem; least-squares approximation; output error; unknown systems; Approximation algorithms; Convergence; Least squares methods; Newton method; Recursive estimation; Stochastic processes;
Conference_Titel :
Industrial Electronics, Control and Instrumentation, 1991. Proceedings. IECON '91., 1991 International Conference on
Conference_Location :
Kobe
Print_ISBN :
0-87942-688-8
DOI :
10.1109/IECON.1991.239055
